hi, guys am new to deeppavlov am trying to upgrade my NER component with few extra data i have after building for a while when i tried to test the actual ner model that has been trained am facing the below error can someone say whats wrong in it.
InvalidArgumentError: Requested more than 0 entries, but params is empty. Params shape: [1,8,0]
heres my json
{
“dataset_reader”: {
“class_name”: “conll2003_reader”,
“data_path”: “{DOWNLOADS_PATH}/ontonotes/”,
“dataset_name”: “ontonotes”,
“provide_pos”: false
},
“dataset_iterator”: {
“class_name”: “data_learning_iterator”
},
“chainer”: {
“in”: [“x”],
“in_y”: [“y”],
“pipe”: [
{
“class_name”: “bert_ner_preprocessor”,
“vocab_file”: “{BERT_PATH}/vocab.txt”,
“do_lower_case”: false,
“max_seq_length”: 512,
“max_subword_length”: 15,
“token_maksing_prob”: 0.0,
“in”: [“x”],
“out”: [“x_tokens”, “x_subword_tokens”, “x_subword_tok_ids”, “pred_subword_mask”]
},
{
“class_name”: “mask”,
“in”: [“x_subword_tokens”],
“out”: [“x_subword_mask”]
},
{
“id”: “tag_vocab”,
“class_name”: “simple_vocab”,
“unk_token”: [“O”],
“pad_with_zeros”: true,
“save_path”: “{NER_PATH}/tag.dict”,
“load_path”: “{NER_PATH}/tag.dict”,
“fit_on”: [“y”],
“in”: [“y”],
“out”: [“y_ind”]
},
{
“class_name”: “bert_ner”,
“n_tags”: “#tag_vocab.len”,
“keep_prob”: 0.1,
“bert_config_file”: “{BERT_PATH}/bert_config.json”,
“pretrained_bert”: “{BERT_PATH}/bert_model.ckpt”,
“attention_probs_keep_prob”: 0.5,
“use_crf”: true,
“return_probas”: false,
“ema_decay”: 0.9,
“encoder_layer_ids”: [-1],
“weight_decay_rate”: 1e-6,
“learning_rate”: 1e-2,
“bert_learning_rate”: 2e-5,
“min_learning_rate”: 1e-7,
“learning_rate_drop_patience”: 30,
“learning_rate_drop_div”: 1.5,
“load_before_drop”: false,
“clip_norm”: 1.0,
“save_path”: “{NER_PATH}/model”,
“load_path”: “{NER_PATH}/model”,
“in”: [“x_subword_tok_ids”, “x_subword_mask”, “pred_subword_mask”],
“in_y”: [“y_ind”],
“out”: [“y_pred_ind”]
},
{
“ref”: “tag_vocab”,
“in”: [“y_pred_ind”],
“out”: [“y_pred”]
}
],
“out”: [“x_tokens”, “y_pred”]
},
“train”: {
“epochs”: 30,
“batch_size”: 16,
“metrics”: [
{
“name”: “ner_f1”,
“inputs”: [“y”, “y_pred”]
},
{
“name”: “ner_token_f1”,
“inputs”: [“y”, “y_pred”]
}
],
“validation_patience”: 100,
“val_every_n_batches”: 20,
“log_every_n_batches”: 20,
“tensorboard_log_dir”: “{NER_PATH}/logs”,
“pytest_max_batches”: 2,
“pytest_batch_size”: 8,
“show_examples”: false,
“evaluation_targets”: [“valid”, “test”],
“class_name”: “nn_trainer”
},
“metadata”: {
“variables”: {
“ROOT_PATH”: “~/.deeppavlov”,
“DOWNLOADS_PATH”: “{ROOT_PATH}/downloads”,
“MODELS_PATH”: “.deeppavlov/checkState/NewModel”,
“BERT_PATH”: “{DOWNLOADS_PATH}/bert_models/multi_cased_L-12_H-768_A-12”,
“NER_PATH”: “{MODELS_PATH}/NERData”
},
“requirements”: [
“{DEEPPAVLOV_PATH}/requirements/tf.txt”,
“{DEEPPAVLOV_PATH}/requirements/bert_dp.txt”
],
“labels”: {
“telegram_utils”: “NERCoNLL2003Model”,
“server_utils”: “NER”
},
“download”: [
{
“url”: “http://files.deeppavlov.ai/deeppavlov_data/ner_ontonotes_bert_mult_v1.tar.gz”,
“subdir”: “{MODELS_PATH}”
},
{
“url”: “http://files.deeppavlov.ai/deeppavlov_data/bert/multi_cased_L-12_H-768_A-12.zip”,
“subdir”: “{DOWNLOADS_PATH}/bert_models”
}
]
}
}